This paper presents a novel approach for extracting fiber paths from the optimized lamination parameters (LPs) of variable stiffness laminated shells, utilizing the framework of physics-informed neural network (PINN)....
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This paper presents a novel approach for extracting fiber paths from the optimized lamination parameters (LPs) of variable stiffness laminated shells, utilizing the framework of physics-informed neural network (PINN). In this methodology, each fiber layer is associated with a specific stream function, which is approximated by an independent neural network. The stream function is governed by a partial differential equation (PDE) derived from the fiber orientation field in the parameter space. Moreover, the isocontours of the stream function are transformed into the actual fiber paths in the physical space. To account for manufacturing constraints, Riemannian geometry serves as a computational tool to determine the intrinsic distance between adjacent fiber paths and the geodesic curvature of the isocontours. By incorporating regularization terms into the loss function based on the physical relationships, the constrained optimization problem is converted into an unconstrained one, making it more suitable for neural network training. Meanwhile, a fiber path extraction (FPE) algorithm is used to minimize the loss function at randomly sampled points through gradient descent. The numerical results suggest that the extraction of fiber paths using PINN can achieve satisfactory levels of accuracy while effectively satisfying the imposed constraints.
This study explores the impact of an innovative three-wire fusion nozzle electroslag welding (FNESW) technique on the microstructural evolution and tensile properties of U75V pearlitic steel rail weld joints. An intel...
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This study explores the impact of an innovative three-wire fusion nozzle electroslag welding (FNESW) technique on the microstructural evolution and tensile properties of U75V pearlitic steel rail weld joints. An intelligent monitoring system was developed to systematically capture critical welding parameters, including current, voltage, cooling rate, and magnetic field intensity. Furthermore, a Back Propagation (BP) neural network model was designed and trained to predict the microstructural features and mechanical properties of the welded joints. The model exhibited robust predictive performance, effectively establishing the quantitative relationship between welding parameters and joint performance. Experimental validation corroborated the model's reliability, with relative errors of key predictive indicators maintained below 15%. The findings provide a scientific basis for optimizing welding parameters and designing high-performance steel rail weld joints through the integration of machine learning techniques, offering new insights into the intelligent control of welding processes.
The regional power grid that relies on the coordinated operation of internal generators and feed-in power through transmission lines (TLs), referred to as a semi-independent power grid (SIPG), enjoys potential selfsus...
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The regional power grid that relies on the coordinated operation of internal generators and feed-in power through transmission lines (TLs), referred to as a semi-independent power grid (SIPG), enjoys potential selfsustaining ability under TL outage events induced by typhoon disasters by self-regulating. However, due to the instantaneous power failure of TLs resulting from physical damage, which is far shorter than the adjustment time of internal generators, SIPG will presumably collapse facing sudden massive power shortage without a previous resource scheduling policy. To ensure the safe operation of SIPG under typhoon disasters, a preventive typhoon-defending scheme is proposed. First, derived from the idea of Few-Shot Class-Incremental Learning (FsciL), a risk prediction model is constructed to fairly assess the outage probability of TLs in the absence of tower damage samples. Noteworthily, real features of towers are extracted. Thus, convincing decision-making is provided for the preventive scheduling strategy. Subsequently, a multistage stochastic optimizing strategy considering the outage probability of TLs is proposed. Specifically, generators and loads in SIPG are pre- scheduled to reduce the interactive power demand from the main grid, alleviating the instantaneous power shortage caused by potential TL outage events. The modified IEEE 24-reliability test system is used to validate the proposed scheme.
PurposeThe purpose of this paper is to address the problem of construction task recruitment and worker job finding on labor platforms, using an improved multi-preference two-sided matching algorithm and blockchain tec...
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PurposeThe purpose of this paper is to address the problem of construction task recruitment and worker job finding on labor platforms, using an improved multi-preference two-sided matching algorithm and blockchain technology to provide mutually satisfactory matches for users in a trusted ***/methodology/approachThis study adopts a stable matching method to consider the preferences of both construction tasks and workers. To ensure the trustworthiness of the platform environment, a dual-chain blockchain system combining public and private chains is designed to improve operational efficiency and scalability. The blockchain-based construction labor matching scheme is tested and compared with other methods to verify its feasibility and *** analysis results show that the proposed mechanism improves the fairness of matching by considering two-sided preferences. The proposed blockchain-based scheme corresponds to the speed in seconds and costs about $1, which is ***/valueThe contribution of this paper is to carry out a preliminary study on the relatively blank field of the current construction labor resource allocation at the platform level and to prompt the expansion of construction labor allocation investigation from the project internal to the market level. Furthermore, the designed B-CLSP model not only considers both construction worker and task preferences to improve matching fairness but also incorporates blockchain technology to improve platform trust and provide guidance for construction labor platform building and development.
Emerging persistent memory (PM) can provide large persistent capacity with performance comparable to DRAM in modern memory systems. Persistent transactional memory (PTM) needs to ensure data consistency after unexpect...
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Emerging persistent memory (PM) can provide large persistent capacity with performance comparable to DRAM in modern memory systems. Persistent transactional memory (PTM) needs to ensure data consistency after unexpected power loss or crashes. Therefore, crash consistency strategies, such as persistent logging, are still required. However, the additional overhead introduced by these strategies, such as significant extra writes on PM, can lead to system performance degradation. In this article, we propose CodePM, a fault-tolerant PM transactional library that utilizes parity-based crash consistency to remove logging overhead while guaranteeing the correct state of data. CodePM reuses the decoding capability of parity to detect and recover inconsistent objects. To ensure consistency without logs when updating, CodePM employs fine-grained memory fences to carefully align potential inconsistency with the repairability of parity. To detect inconsistency without logs when recovering, CodePM utilizes optimistic speculative scanning recovery by reusing checksum and parity, which supports instant recovery with transient degraded reliability. Moreover, we study the memory fence blocking effects and further augment CodePM with pipelined encoding and persistent writing to hide update latency. We implemented CodePM on Pangolin, the state-of-the-art parity-based PTM for fault-tolerance. Evaluation results with real-world workloads on Intel Optane DCPMM show that CodePM can achieve up to $3.4\times $ higher throughput than Pangolin.
As accurate face recognition (FR) models based on deep learning can be easily trained using face images from various social media platforms, this phenomenon has raised ever-increasing concerns regarding user privacy. ...
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As accurate face recognition (FR) models based on deep learning can be easily trained using face images from various social media platforms, this phenomenon has raised ever-increasing concerns regarding user privacy. To address this issue, we investigate a privacy protection scheme based on multifeature fusion tensor (PPS-MFFT). Different from previous studies using a single feature or simple combination of several features, for every face image, PPS-MFFT first constructs a multifeature fusion tensor through hierarchically exploiting the correlations and complementarity between deep-learning features and those handcrafted features for stronger robustness and transferability. Further, on the basis of such tensors, the target images are reasonably chosen to enhance the camouflage effects while maintaining the visual similarities for final perturbed images, which are generated by means of developing a new optimization model for better tradeoff between effectiveness and practicability. Finally, the measurement results validate that both higher protective efficacy (e.g., 16% more in misidentifying the original face images) and acceptable visual effects can be obtained by PPS-MFFT when compared to the existing methods, and thus demonstrate the generality and applicability of our scheme.
As iptycenes of arenes are fused to a bicyclo[2.2.2]octatriene bridgehead system, there are only odd-sequenced iptycene family members, such as triptycene, pentiptycene and heptiptycene. In order to ensure the complet...
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As iptycenes of arenes are fused to a bicyclo[2.2.2]octatriene bridgehead system, there are only odd-sequenced iptycene family members, such as triptycene, pentiptycene and heptiptycene. In order to ensure the completeness of the iptycene family sequence, developing even-sequenced iptycene family members is of great significance. The dimer of anthracene derivatives is a class of tricyclo[2.2.2.2]dodetetraene molecules with four separate phenyl rings, which are structurally similar to the iptycene family and herein referred to as "tetraptycene". In this work, a series of hydroxyl or methoxy-substituted tetraptycene derivatives from the photochemical reactions of anthracene derivatives was reported. These tetraptycene derivatives were characterized using nuclear magnetic resonance (NMR), mass spectrum (MS) and single-crystal X-ray diffraction (SC-XRD). Moreover, their self-assemblies in the solid state were further discussed. Their properties of modifiability, asymmetry, and rigidity indicate their superiority as novel monomers to construct functional material architectures.
Excessive bilirubin poses a significant risk factor in the progression of chronic liver disease. However, due to its nature as an albumin-bound toxin, bilirubin cannot be efficiently eliminated through conventional he...
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Excessive bilirubin poses a significant risk factor in the progression of chronic liver disease. However, due to its nature as an albumin-bound toxin, bilirubin cannot be efficiently eliminated through conventional hemodialysis therapy or existing hemoperfusion adsorbents, presenting a challenge in selective removal. It is widely acknowledged that adjusting the pore properties of adsorbents can impact the adsorption efficiency of a hemoperfusion adsorbent. However, few studies have been working on improving the selectivity of bilirubin removal through precise regulation of pore size. In this paper, we aim to demonstrate that the selectivity of bilirubin removal can be achieved and enhanced by fine-tuning the pore size of ordered mesoporous materials. Ordered mesoporous SiO2 (OMS) is selected as the research object due to its highly organized and uniform porous structure, as well as its excellent biocompatibility. The results indicate that OMS nanoparticles with a pore diameter of 2.5 nm (OMS-2.5nm) exhibit superior adsorption capacity for bilirubin in both pure and albuminrich solutions, suggesting the potential for achieving efficient and selective bilirubin removal through pore size optimization. Furthermore, to demonstrate the feasibility of OMS nanoparticles in practical applications (i.e. their inheritability of selectivity), we engineered OMS nanoparticles into polyvinyl alcohol (PVA) microspheres, forming OMS/PVA composite microspheres. The incorporation of OMS nanoparticles significantly enhances the bilirubin adsorption capacity of PVA microspheres while simultaneously reducing their albumin adsorption. The excellent selective adsorption efficacy of OMS-2.5nm is preserved in the composite microspheres, underscoring its potential therapeutic benefits for treating diseases with excessive bilirubin levels.
Pressureless sintered silicon carbide (PS-SiC) ceramics are widely used as friction materials in the aerospace industry, and enhancing the self-lubricating properties of PS-SiC ceramics under dry friction is highly si...
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Pressureless sintered silicon carbide (PS-SiC) ceramics are widely used as friction materials in the aerospace industry, and enhancing the self-lubricating properties of PS-SiC ceramics under dry friction is highly significant. In this study, an infrared femtosecond laser was used to treat the surface of PS-SiC ceramics, and the effects of various processing parameters on surface microstructure, chemical composition, and graphitization degree were investigated. More importantly, SiC decomposes into amorphous carbon and stays on the surface of PS-SiC ceramics under the photothermal effect, and the amorphous carbon realizes the transition to the ordered graphite structure by controlling the laser energy. The highly graphitized, carbon-containing micro/nanostructures on the surface of laser-treated PS-SiC ceramics promote the formation of stable carbon-based tribofilms during sliding, which significantly enhances the tribological properties of PS-SiC ceramics under dry friction. This study proposes a method for inducing graphitization on the surface of PS-SiC ceramics using an infrared femtosecond laser, providing a manufacturing approach and theoretical support for the development of high-performance PS-SiC ceramic friction materials.
This study elucidates the reinforcement mechanisms of TC4/carbon fiber reinforced thermoplastic (CFRTP) joints using an oscillating laser beam based on the analysis of interfacial heat and mass transfer behaviors. Und...
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This study elucidates the reinforcement mechanisms of TC4/carbon fiber reinforced thermoplastic (CFRTP) joints using an oscillating laser beam based on the analysis of interfacial heat and mass transfer behaviors. Under the same laser line energy, oscillating laser joined (OLJ) TC4/CFRTP joints exhibited superior joint morphology, a more stable joining process, and increased joint strength compared to laser direct joined (LDJ) joints. At a laser line energy of 50 J/mm (500 W, 10 mm/s), the oscillating laser beam increased joint strength from 970 N to 1578.75 N, representing a 62.76 % improvement. Analysis of experimental and simulation results indicated that the primary mechanism driving this enhancement is the altered heat transfer behavior caused by the oscillating laser beam. Spatially, the laser beam action distance is reduced at the center of the joining area while expanding on both sides. Temporally, the pyrolysis duration of CFRTP at the interface is shortened, while the melting duration is extended. Consequently, the pyrolysis zone diminishes or disappears entirely, whereas the melting zone expands. Both LDJ and OLJ joints exhibit similar mass transfer behavior, forming a 6 mu m thick element diffusion layer containing CTi0.42V1.58 and TiOS at the interface. In conclusion, the oscillating laser beam promotes uniform energy distribution, enhancing CFRTP melting without pyrolysis, thereby strengthening joints without compromising processing efficiency.
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